Reconfigurable Field Programmable Analog Array–Based Filtering of Electrocardiogram Signals

This paper aimed to develop a reconfigurable, adaptable hardware platform based on a field programmable analog array (FPAA) for filtering electrocardiogram (ECG) signals. The system enables the redesign of analog filtering parameters to suit signal shape or medical needs. The ECG signals are acquired using Einthoven’s threelead configuration and converted into raw digital data with the BIOPAC MP35 device (BIOPAC Systems, Inc., Goleta, CA, USA). To convert the digital raw ECG signal to an analog signal, a field-programmable gate array (FPGA) device is employed, enabling simultaneous operation with an R-2R ladder digital-to-analog converter. The FPGA converts the raw digital ECG signal into an analog signal. Consequently, integration of FPAA and FPGA devices forms an adaptable biosignal acquisition and filtering platform. Surface ECG signal acquisition is inherently vulnerable to noise due to the microvolt-level amplitude and low-frequency spectrum (0.5–100 Hz) of cardiac biopotentials. Major interferences arise from both physiological sources—such as respiratory baseline wandering and muscle activity—and environmental factors, including 50/60 Hz power-line hum, equipment noise, and poor electrode contact. Left unfiltered, these disturbances distort essential waveform components (P wave, QRS complex, and T wave), compromising signal integrity and increasing the risk of clinical misdiagnosis. The FPAA platform combines software, hardware, and algorithms into a single compact unit. Traditionally, ECG signal filtering requires an analog stage for amplification and filtering. Recently, FPAA has emerged as a flexible, rapid-prototyping platform for developing complex analog acquisition and filtering systems for ECG monitoring. This study introduces a method for filtering ECG signals using the FPAA device, which is based on Anadigm filter technology. The filter types (low-pass [LP], high-pass [HP], and notch filter [NF]) for ECG signals are configured using AnadigmDesigner2 on a computer and programmed into the hardware. The fully filtered FPAA output achieved an ensemble-based signalto- noise ratio of 24.48 dB, corresponding to a 7.82 dB improvement relative to the raw ECG. The device’s filtering capability was evaluated using an FPAA board and compared with Biopac simulation results. The experimental results demonstrate the functional operation of the proposed FPAA-based filtering platform.